app.py

import gradio as gr
import pandas as pd
import numpy as np
import json
from io import StringIO
from collections import OrderedDict



import os

# ---------------------- Accessing data from Notion ---------------------- #


from notion_client import Client as client_notion

notionToken = os.getenv('notionToken')
if notionToken is None:
    raise Exception("Notion token not found. Please check the environment variables.")
else:
    print("Notion token found successfully!")

from config import landuseDatabaseId , subdomainAttributesDatabaseId 
from imports_utils import fetch_all_database_pages
from imports_utils import get_property_value
from imports_utils import notion

landuse_attributes  = fetch_all_database_pages(notion, landuseDatabaseId)
livability_attributes  = fetch_all_database_pages(notion, subdomainAttributesDatabaseId)


# fetch the dictionary with landuse - domain pairs 
landuseMapperDict ={}
subdomains_unique = []

for page in landuse_attributes:
    value_landuse = get_property_value(page, "LANDUSE")
    value_subdomain = get_property_value(page, "SUBDOMAIN_LIVEABILITY")
    if value_subdomain and value_landuse:
        landuseMapperDict[value_landuse] = value_subdomain
    if value_subdomain != "":
        subdomains_unique.append(value_subdomain)



# fetch the dictionary with subdomain attribute data
attributeMapperDict ={}
domains_unique = []

for page in livability_attributes:
    subdomain = get_property_value(page, "SUBDOMAIN_UNIQUE")
    sqm_per_employee = get_property_value(page, "SQM PER EMPL")
    thresholds = get_property_value(page, "MANHATTAN THRESHOLD")
    max_points = get_property_value(page, "LIVABILITY MAX POINT")
    domain = get_property_value(page, "DOMAIN")
    if  thresholds:   
        attributeMapperDict[subdomain] = {
        'sqmPerEmpl': sqm_per_employee if sqm_per_employee != "" else 0,
        'thresholds': thresholds,
        'max_points': max_points,
        'domain': [domain if domain != "" else 0]
        }
    if domain != "":
        domains_unique.append(domain)




# ---------------------- Accessing data from Speckle ---------------------- #


from specklepy.api.client import SpeckleClient
from specklepy.api.credentials import get_default_account, get_local_accounts
from specklepy.transports.server import ServerTransport
from specklepy.api import operations
from specklepy.objects.geometry import Polyline, Point
from specklepy.objects import Base

import imports_utils
import speckle_utils
import data_utils

from config import landuseDatabaseId , streamId,  dmBranchName, dmCommitId, luBranchName, luCommitId
from imports_utils import speckleToken
from imports_utils import fetchDistanceMatrices
from config import distanceMatrixActivityNodes
from config import distanceMatrixTransportStops

CLIENT = SpeckleClient(host="https://speckle.xyz/")
account = get_default_account()
CLIENT.authenticate_with_token(token=speckleToken)

streamDistanceMatrices = speckle_utils.getSpeckleStream(streamId,dmBranchName,CLIENT, dmCommitId)
matrices = fetchDistanceMatrices (streamDistanceMatrices)

streamLanduses = speckle_utils.getSpeckleStream(streamId,luBranchName,CLIENT, luCommitId)
streamData = streamLanduses["@Data"]["@{0}"]
df_speckle_lu = speckle_utils.get_dataframe(streamData, return_original_df=False)
df_lu = df_speckle_lu.copy()
# set index column
df_lu =  df_lu.set_index("ids", drop=False)

df_dm = matrices[distanceMatrixActivityNodes]
df_dm_transport = matrices[distanceMatrixTransportStops]

dm_dictionary = df_dm.to_dict('index')
df_dm_transport_dictionary = df_dm_transport.to_dict('index')


# filter activity nodes attributes
mask_connected = df_dm.index.tolist()
lu_columns = []
for name in df_lu.columns:
  if name.startswith("lu+"):
    lu_columns.append(name)
df_lu_filtered = df_lu[lu_columns].loc[mask_connected]
df_lu_filtered

df_lu_filtered_dict = df_lu_filtered.to_dict('index')




def test(input_json):
    print("Received input")
    # Parse the input JSON string
    try:
        inputs = json.loads(input_json)
    except json.JSONDecodeError:
        inputs = json.loads(input_json.replace("'", '"'))


    
    # Accessing input data from Grasshopper
    
    #if df_dm is None:
    matrix = inputs['input']["matrix"]
        
    #if df_dm_transport is None:
    matrix_transport = inputs['input']["transportMatrix"]  
    
    #if df_lu_filtered is None:
    landuses = inputs['input']["landuse_areas"]    
    
    attributeMapperDict_gh = inputs['input']["attributeMapperDict"]
    landuseMapperDict_gh = inputs['input']["landuseMapperDict"]
    
    alpha = inputs['input']["alpha"]
    alpha = float(alpha)
    threshold = inputs['input']["threshold"]
    threshold = float(threshold)
    
    df_matrix = pd.DataFrame(matrix).T
    df_matrix = df_matrix.round(0).astype(int)

    df_landuses = pd.DataFrame(landuses).T
    df_landuses = df_landuses.round(0).astype(int)


    from imports_utils import splitDictByStrFragmentInColumnName
    
    # List containing the substrings to check against
    tranportModes = ["DRT", "GMT", "HSR"]

    result_dicts = splitDictByStrFragmentInColumnName(df_dm_transport_dictionary, tranportModes)

    # Accessing each dictionary
    art_dict = result_dicts["DRT"]
    gmt_dict = result_dicts["GMT"]

    df_art_matrix = pd.DataFrame(art_dict).T
    df_art_matrix = df_art_matrix.round(0).astype(int)  
    df_gmt_matrix = pd.DataFrame(gmt_dict).T
    df_gmt_matrix = df_art_matrix.round(0).astype(int)     
    

    # create a mask based on the matrix size and ids, crop activity nodes to the mask
    mask_connected = df_dm.index.tolist()

    valid_indexes = [idx for idx in mask_connected if idx in df_landuses.index]
    # Identify and report missing indexes
    missing_indexes = set(mask_connected) - set(valid_indexes)
    if missing_indexes:
        print(f"Error: The following indexes were not found in the DataFrame: {missing_indexes}, length: {len(missing_indexes)}")
    
    # Apply the filtered mask
    df_landuses_filtered = df_landuses.loc[valid_indexes]


    # find a set of unique domains, to which subdomains are aggregated    
    temp = []
    for key, values in attributeMapperDict.items():
      domain = attributeMapperDict[key]['domain']
      for item in domain:
        if ',' in item:
          domain_list = item.split(',')
          attributeMapperDict[key]['domain'] = domain_list
          for domain in domain_list:
            temp.append(domain) 
        else:
          if item != 0: 
              temp.append(item)  
    
    domainsUnique = list(set(temp))


    # find a list of unique subdomains, to which land uses are aggregated
    temp = []    
    for key, values in landuseMapperDict.items():
      subdomain = str(landuseMapperDict[key])
      if subdomain != 0: 
        temp.append(subdomain) 
        
    subdomainsUnique = list(set(temp))
    

    
    
    from imports_utils import landusesToSubdomains

    """
    def landusesToSubdomains(DistanceMatrix, LanduseDf, LanduseToSubdomainDict, UniqueSubdomainsList):
        df_LivabilitySubdomainsArea = pd.DataFrame(0, index=DistanceMatrix.index, columns=UniqueSubdomainsList)
    
        for subdomain in UniqueSubdomainsList:
            for lu, lu_subdomain in LanduseToSubdomainDict.items():
                if lu_subdomain == subdomain:
                    if lu in LanduseDf.columns:
                        df_LivabilitySubdomainsArea[subdomain] = df_LivabilitySubdomainsArea[subdomain].add(LanduseDf[lu], fill_value=0)
                    else:
                        print(f"Warning: Column '{lu}' not found in landuse database")
    
        return df_LivabilitySubdomainsArea

    
    """
    LivabilitySubdomainsWeights = landusesToSubdomains(df_dm,df_landuses_filtered,landuseMapperDict,subdomainsUnique)
    



    
    def FindWorkplaces (DistanceMatrix,SubdomainAttributeDict,destinationWeights,UniqueSubdomainsList ):
        
        df_LivabilitySubdomainsWorkplaces = pd.DataFrame(0, index=DistanceMatrix.index, columns=['jobs'])
    
        for subdomain in UniqueSubdomainsList:
          for key, value_list in SubdomainAttributeDict.items():
            sqm_per_empl = float(SubdomainAttributeDict[subdomain]['sqmPerEmpl'])  #[0])
            if key in destinationWeights.columns and key == subdomain:
              if sqm_per_empl > 0:
                df_LivabilitySubdomainsWorkplaces['jobs'] += (round(destinationWeights[key] / sqm_per_empl,2)).fillna(0)
              else:
                df_LivabilitySubdomainsWorkplaces['jobs'] += 0
    
        return df_LivabilitySubdomainsWorkplaces


    WorkplacesNumber = FindWorkplaces(df_dm,attributeMapperDict,LivabilitySubdomainsWeights,subdomainsUnique)
    
    # prepare an input weights dataframe for the parameter LivabilitySubdomainsInputs
    LivabilitySubdomainsInputs =pd.concat([LivabilitySubdomainsWeights, WorkplacesNumber], axis=1)

    
    
    def computeAccessibility (DistanceMatrix, destinationWeights=None,alpha = 0.0038, threshold = 600):
    
        decay_factors = np.exp(-alpha * DistanceMatrix) * (DistanceMatrix <= threshold)
        
        # for weighted accessibility (e. g. areas)
        if destinationWeights is not None: #not destinationWeights.empty:
            subdomainsAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=destinationWeights.columns)
            for col in destinationWeights.columns:
                subdomainsAccessibility[col] = (decay_factors * destinationWeights[col].values).sum(axis=1)
        else:
            print("Destination weights parameter is None")
        
        return subdomainsAccessibility
    



    def computeAccessibility_pointOfInterest (DistanceMatrix, columnName, alpha = 0.0038, threshold = 600):
    
        decay_factors = np.exp(-alpha * DistanceMatrix) * (DistanceMatrix <= threshold)

        pointOfInterestAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=[columnName])
        for col in pointOfInterestAccessibility.columns:
            pointOfInterestAccessibility[col] = (decay_factors * 1).sum(axis=1)
        
        return pointOfInterestAccessibility
    
    
    
    subdomainsAccessibility = computeAccessibility(df_dm,LivabilitySubdomainsInputs,alpha,threshold)   
    artAccessibility = computeAccessibility_pointOfInterest(df_art_matrix,'ART',alpha,threshold)
    gmtAccessibility = computeAccessibility_pointOfInterest(df_gmt_matrix,'GMT+HSR',alpha,threshold)
    
    AccessibilityInputs = pd.concat([subdomainsAccessibility, artAccessibility,gmtAccessibility], axis=1)
    
    

    def remap(value, B_min, B_max, C_min, C_max):
        return C_min + (((value - B_min) / (B_max - B_min))* (C_max - C_min))    


    if 'jobs' not in subdomainsAccessibility.columns:
        print("Error: Column 'jobs' does not exist in the subdomainsAccessibility.")

    
    
    def accessibilityToLivability (DistanceMatrix,accessibilityInputs, SubdomainAttributeDict,UniqueDomainsList):
    
        livability = pd.DataFrame(index=DistanceMatrix.index, columns=accessibilityInputs.columns)
                             
        for domain in UniqueDomainsList:
            livability[domain] = 0
            
        livability.fillna(0, inplace=True)
        
        templist = []
        # remap accessibility to livability points
        
        for key, values in SubdomainAttributeDict.items():
            threshold = float(SubdomainAttributeDict[key]['thresholds'])
            max_livability = float(SubdomainAttributeDict[key]['max_points'])
            domains = [str(item) for item in SubdomainAttributeDict[key]['domain']]
        
            if key in accessibilityInputs.columns and key != 'commercial':
                livability_score = remap(accessibilityInputs[key], 0, threshold, 0, max_livability)
                livability.loc[accessibilityInputs[key] >= threshold, key] = max_livability
                livability.loc[accessibilityInputs[key] < threshold, key] = livability_score          
                if any(domains):
                    for domain in domains:
                        if domain != 'Workplaces':
                            livability.loc[accessibilityInputs[key] >= threshold, domain] += max_livability
                            livability.loc[accessibilityInputs[key] < threshold, domain] += livability_score
                                                
            elif key == 'commercial':
                livability_score = remap(accessibilityInputs['jobs'], 0, threshold, 0, max_livability)
                livability.loc[accessibilityInputs['jobs'] >= threshold, domains[0]] = max_livability
                livability.loc[accessibilityInputs['jobs'] < threshold, domains[0]] = livability_score

        
        return livability
    
    
    

    livability = accessibilityToLivability(df_dm,AccessibilityInputs,attributeMapperDict,domainsUnique)
    

    livability_dictionary = livability.to_dict('index')
    LivabilitySubdomainsInputs_dictionary = LivabilitySubdomainsInputs.to_dict('index')
    subdomainsAccessibility_dictionary = AccessibilityInputs.to_dict('index')

    artmatrix = df_art_matrix.to_dict('index')
    
    # Prepare the output
    output = {
        "subdomainsAccessibility_dictionary": subdomainsAccessibility_dictionary,
        "livability_dictionary": livability_dictionary,
        "subdomainsWeights_dictionary": LivabilitySubdomainsInputs_dictionary,
        "luDomainMapper": landuseMapperDict,
        "attributeMapper": attributeMapperDict,
        "fetchDm": dm_dictionary,
        "artmatrix":artmatrix
    }


    
    return json.dumps(output)

    # Define the Gradio interface with a single JSON input
iface = gr.Interface(
    fn=test,
    inputs=gr.Textbox(label="Input JSON", lines=20, placeholder="Enter JSON with all parameters here..."),
    outputs=gr.JSON(label="Output JSON"),
    title="testspace"
)

iface.launch()